concurve
v2.0

Computes and Plots Consonance (Confidence) Intervals, P-Values,
and S-Values to Form Consonance and Surprisal Functions

Allows one to compute consonance (confidence) intervals for various statistical tests along with their corresponding P-values and S-values. The intervals can be plotted to create consonance and surprisal functions allowing one to see what effect sizes are compatible with the test model at various consonance levels rather than being limited to one interval estimate such as 95%. These methods are discussed by Poole C. (1987) <doi:10.2105/AJPH.77.2.195>, Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>, Singh K, Xie M, Strawderman WE. (2007) <arXiv:0708.0976>, Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>, and Greenland S. (2019) <doi:10.1080/00031305.2018.1529625>.

Readme

concurve

concurve | Graph Interval Functions

In addition to the overt statistical position, the p-value function
also provides easily and accurately many of the familiar types of
summary information: a median estimate of the parameter; a
one-sided test statistic for a scalar parameter value at any
chosen level; the related power function; a lower confidence
bound at any level; an upper confidence bound at any level; and
confidence intervals with chosen upper and lower confidence
limits. The p value reports all the common inference material, but
with high accuracy, basic uniqueness, and wide generality.

From a scientific perspective, the likelihood function and p-value
function provide the basis for scientific judgments by an
investigator, and by other investigators who might have interest. It
thus replaces a blunt yes or no decision by an opportunity for
appropriate informed judgment.” - D. A. S.
Fraser, 2019

Install the Package From CRAN

Install the Developer Version

For Stata:

Dependencies

ggplot2

metafor

parallel

dplyr

tibble

survival

survminer

scales

"Statistical software enables and promotes cargo-cult statistics.
Marketing and adoption of statistical software are driven by ease of
use and the range of statistical routines the software implements.
Offering complex and “modern” methods provides a competitive
advantage. And some disciplines have in effect standardised on
particular statistical software, often proprietary software.

Statistical software does not help you know what to compute, nor how
to interpret the result. It does not offer to explain the assumptions
behind methods, nor does it flag delicate or dubious assumptions. It
does not warn you about multiplicity or p-hacking. It does not check
whether you picked the hypothesis or analysis after looking at the
data, nor track the number of analyses you tried before arriving at
the one you sought to publish – another form of multiplicity. The more
“powerful” and “user-friendly” the software is, the more it invites
cargo-cult statistics." - Stark & Saltelli, 2018